1. Field of the Invention
The present invention relates to process and machine modeling and system monitoring, especially for predictive condition monitoring. More particularly, the invention relates to condition monitoring of a system using an empirical model having inputs derived from complex signal decomposition using a sub-band technique.
2. Description of the Related Art
A number of advanced techniques have been developed to provide improved monitoring and control of equipment and processes, for example in an industrial setting. Such techniques have the potential of providing earlier detection of equipment failure and process upset, as well as finer control, and offer an alternative to simpler, threshold-based alarm condition monitoring systems, which merely examine the raw value of a single sensor signal in isolation against an absolute threshold. One such advanced technique is described in U.S. Pat. No. 4,937,763 to Mott, improved in U.S. Pat. No. 5,764,509 to Gross et al. Therein, an empirical model of proper equipment or process operational states is created from exemplary data, which model can be used in real-time monitoring to generate estimates of expected sensor readings in response to receiving actual sensor readings from equipment or process instrumentation. The expected readings are compared to the actual readings to provide indications of abnormality indicating impending equipment failure or process upset. These techniques are able to provide such indications long before conventional thresholding techniques would detect any problem, and thereby provide valuable lead-time in responding to developing faults.
These techniques take advantage of the information inherent in the (often unknown) relationships between the parameters measured by the sensors, to provide such advanced condition monitoring. As a consequence, these techniques work only in the context of sensor readings for multiple, related (correlated) parameters. Theoretically, at least three sensors are required to build the requisite model, and in practice even more sensors are necessary. This limits the applicability of these advanced techniques to equipment and processes instrumented with multiple sensors for correlated parameters. It would be beneficial to be able to apply these techniques to circumstances where uninstrumented equipment or processes could be retrofitted with just one sensor, such as an acoustic pickup, or to monitoring which generates just one complex signal as a composite picture of system operation, such as an EKG signal for a heart.
In a related field prior art, vibration analysis, methods are known for examining the power spectral density function from an accelerometer or acoustic pickup to provide means for monitoring rotating or cyclic equipment. Typically, frequencies of interest are examined, and thresholds (lower or upper limit) are placed on the power level expected for these frequencies. If a threshold is pierced, this is indicative of an unsatisfactory operating condition or a developing problem. A great deal of work is involved in identifying the frequencies of interest and expected power levels for each particular piece of equipment that is monitored in this fashion. Problem diagnosis is also typically very specific to the kinds of indications presented with the appearance of the particular problem, and must be worked out specifically for each machine. It would be useful to have an empirical data-driven way of determining the health or the operational state of a machine or other process state based on one or more vibration or acoustic signals, data from a process or other figure of merit or state data.
What is needed is a way to apply the advanced empirical modeling techniques for condition monitoring of signals from any “monitored system” including without limitation: equipment or processes, such as those signals that are monitored in the field of vibration monitoring, and further in the medical arts, the biological field generally, the chemical technology area, business and financial fields, mechanical arts generally, meteorology and any process providing data susceptible of condition monitoring.